Fraunhofer-Institut für Produktionsanlagen und Konstruktionstechnik IPK

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  • Publication
    Development of a mechanical, biocide-free method of disinfection for cathodic dip coating processes
    ( 2021)
    Uhlmann, Eckart
    ;
    Hilt, Michael
    ;
    Hein, Christoph
    ;
    Cudazzo, Markus
    ;
    Huth-Herms, Katrin
    ;
    Jahnke, Christian
    ;
    Quast, Melanie
    Technical fluids are often contaminated by bacteria as Burkholderia cepacia (B. cepacia), which is found in different industrial issues and affects manufacturing process chains by the formation of planktonic cell-aggregates and biofilms within the working fluids. B. cepacia is one of nine species of the Burkholderia cepacia complex (Bcc), a group of gram-negative, motile, non-spore-forming, and rod-shaped bacteria. Because of the opportunistic pathogenicity to plants, animals, humans, and the multi-drug and biocide resistance, B. cepacia is difficult to treat. This study aims to provide an application to reduce the germ numbers ng in an eco-friendly and continuous process without the use of biocides. The approach to disinfect technical fluids is to apply high shear forces in a rotor-stator assembly to the fluid. A prototype of the rotor-stator assembly with a variably adjustable shear gap gs and rotor speed srot was constructed. First experiments with a frequency frot 1 0 Hz ⤠frot ⤠40 Hz a shear gap gs = 83 µm and gs = 166 µm showed a reduction of germ number ngr = 99.6 %. It concluded that the disinfection of technical fluids by a rotor-stator assembly is a biocide-free alternative. In addition to defined process parameters such as shear gap gS, temperature Ï, frequency frot and time of machining process tmp, also the peripheral speed vp, rotational speed vrot, flow rate fr and shear stresses Ï were used to assess the machining result and to develop an overall concept for disinfection of technical fluids.
  • Publication
    A Process Model for Enhancing Digital Assistance in Knowledge-Based Maintenance
    ( 2019)
    Kovacs, Klaudia
    ;
    Ansari, Fazel
    ;
    Geisert, Claudio
    ;
    Uhlmann, E.
    ;
    Glawar, Robert
    ;
    Sihn, Wilfried
    Digital transformation and evolution of integrated computational and visualisation technologies lead to new opportunities for reinforcing knowledge-based maintenance through collection, processing and provision of actionable information and recommendations for maintenance operators. Providing actionable information regarding both corrective and preventive maintenance activities at the right time may lead to reduce human failure and improve overall efficiency within maintenance processes. Selecting appropriate digital assistance systems (DAS), however, highly depends on hardware and IT infrastructure, software and interfaces as well as information provision methods such as visualization. The selection procedures can be challenging due to the wide range of services and products available on the market. In particular, underlying machine learning algorithms deployed by each product could provide certain level of intelligence and ultimately could transform diagnostic maintenance capabilities into predictive and prescriptive maintenance. This paper proposes a process-based model to facilitate the selection of suitable DAS for supporting maintenance operations in manufacturing industries. This solution is employed for a structured requirement elicitation from various application domains and ultimately mapping the requirements to existing digital assistance solutions. Using the proposed approach, a (combination of) digital assistance system is selected and linked to maintenance activities. For this purpose, we gain benefit from an in-house process modeling tool utilized for identifying and relating sequence of maintenance activities. Finally, we collect feedback through employing the selected digital assistance system to improve the quality of recommendations and to identify the strengths and weaknesses of each system in association to practical use-cases from TU Wien Pilot-Factory Industry 4.0.